Satvik Sharma

I'm a 2nd year PhD at Stanford AI Lab, co-advised by Jeannette Bohg and Dorsa Sadigh. My research focuses on dexterous manipulation and on incorporating foundation models into manipulation policies. Before Stanford, I was at Berkeley Artificial Intelligence Research (BAIR) where I was advised by Prof. Ken Goldberg. I did my Bachelor's degree at Berkeley in CS.

Email/Google Scholar/Github/Linkedin

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Selected Publications

One Demonstration, Many Objects: Generalizing Manipulation via Local Contact Geometry

Satvik Sharma*, Samrat Sahoo*, Huang Huang*, Fei-Fei Li, Jiajun Wu, Dorsa Sadigh, Jeannette Bohg

arXiv, 2026

Website/PDF

Pre-training Visual Dexterity in Simulation

Sarthak Kamat*, Adam Rashid*, Satvik Sharma, Aseem Doriwala, Chelsea Finn, Phillip Isola, C. Karen Liu

arXiv, 2026

Website/PDF/Bibtex

MemER: Scaling Up Memory for Robot Control via Experience Retrieval

Ajay Sridhar*, Jennifer Pan*, Satvik Sharma, Chelsea Finn

International Conference on Learning Representations (ICLR), 2026

Website/PDF/Code/Bibtex

Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping

Adam Rashid*, Satvik Sharma*, Chung Min Kim, Justin Kerr, Lawrence Yunliang Chen, Angjoo Kanazawa, Ken Goldberg (* Denotes Equal Contribution, Alphabetically Ordered)

Conference on Robot Learning (CoRL), 2023 - Best Paper Finalist

Website/PDF/Bibtex

Open-World Semantic Mechanical Search with Large Vision and Language Models

Satvik Sharma*, Huang Huang*, Kaushik Shivakumar, Lawrence Yunliang Chen, Ryan Hoque, Brian Ichter, Ken Goldberg

Conference on Robot Learning (CoRL), 2023

PDF/Bibtex

Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision

Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg

Conference on Robot Learning (CoRL), 2022 - Oral Presentation

Website/PDF/Bibtex

Policy Gradient Bayesian Robust Optimization for Imitation Learning

Zaynah Javed*, Daniel Brown*, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg

International Conference on Machine Learning (ICML), 2021

Website/PDF/Bibtex